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UID:pretalx-juliacon-2026-QTDH38@pretalx.com
DTSTART;TZID=CET:20260813T121500
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DESCRIPTION:Phylogenetic reconstruction and comparative analysis are fundam
 ental to understanding evolutionary relationships and biological diversity
 . Traditional algorithms rely heavily on multiple sequence alignments and 
 statistical modelling\, which face significant computational challenges wi
 th large-scale datasets. Furthermore\, integrating information from multip
 le data sources\, such as sequences\, structures\, and functional annotati
 ons\, at the time of reconstruction\, remains technically challenging\, li
 miting the feasibility of phylogenetic reconstruction using today’s dive
 rsity of biological annotations.\n\nHyperdimensional computing (HDC) is a 
 novel computational paradigm that employs high-dimensional representations
  of atomic entities (e.g.\, amino acids) and combines them via algebraic o
 perations to represent more complex data structures (e.g.\, proteins). Thi
 s paradigm\, parallel to connectionist modelling\, is characterised by mod
 elling the brain's distributed memory and the operations underlying its pr
 ocessing. HDC exhibits several properties advantageous for biological data
  analysis: robustness to noise\, holographic information distribution\, an
 d the ability to integrate heterogeneous data sources seamlessly. Recent a
 pplications in DNA sequencing\, pattern matching\, and molecular classific
 ation have demonstrated HDC's potential in bioinformatics\, where its comp
 utational efficiency\, interpretability\, and natural capacity for multimo
 dal data fusion make it particularly well suited to complex phylogenetic a
 nalyses.\n\nIn this talk\, we showcase the potential of HDC for phylogenet
 ic reconstruction and comparative analysis. Here\, we present PhyloHD.jl\,
  a Julia package for representing biological data as hypervectors and reco
 nstructing phylogenetic trees from these representations. We will showcase
  how to calculate branch support using the HDC paradigm and present a mult
 imodal tree reconstruction approach that integrates multiple heterogeneous
  data sources\, including sequences\, structures\, and functional annotati
 ons. Finally\, we will showcase how HDC learning techniques can be used fo
 r family-based phylogenetic tree reconstruction and ancestral sequence rec
 onstruction. This work represents the first attempt to use hyperdimensiona
 l computing as a computational paradigm for phylogenetics and opens new av
 enues for research in this field.
DTSTAMP:20260529T230840Z
LOCATION:Room 4
SUMMARY:PhyloHD.jl: Hyperdimensional Computing meets phylogenetic reconstru
 ction - Carlos Vigil-Vásquez
URL:https://pretalx.com/juliacon-2026/talk/QTDH38/
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